Abstract
This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29–30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.
| Original language | English |
|---|---|
| Pages (from-to) | 185–203 |
| Number of pages | 19 |
| Journal | Journal of Revenue and Pricing Management |
| Volume | 18 |
| Issue number | 3 |
| Early online date | 16 Oct 2018 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
Funding
Acknowledgements Chris Bayliss and Christine Currie were funded by the EPSRC under Grant Number EP/N006461/1. Andria Ellina and Simos Zachariades were part funded by EPSRC as part of their PhD studentships. Asbjørn Nilsen Riseth was partially funded by EPSRC Grant EP/L015803/1.
| Funders | Funder number |
|---|---|
| Engineering and Physical Sciences Research Council | EP/N006461/1, EP/L015803/1 |
Keywords
- Competition
- Dynamic pricing
- Learning
- Numerical performance
Fingerprint
Dive into the research topics of 'Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver